Autonomous vehicles are programmed to automatically drive through a road based on pre-defined conditions. Such vehicles also utilize real-time sensors and processing systems to identify current road conditions and make intelligent decisions about guiding the autonomous vehicles based on road region. Therefore, the sensors and processing systems need lot of input data processing capabilities for identifying the road region based on different type of roads. The different type of road conditions can be based on terrain of the road, vehicles present on the road, other vehicles travelling on the same road, other vehicle's position w.r.t to the autonomous vehicle etc. and sometimes other vehicle's movement around the autonomous vehicle can also be varying.
Further, there can be situations where the road region is entirely occluded by objects and no road region is visible to the system. Considering all above situations, the autonomous vehicle system should be intelligent enough not to map the occluded objects as drivable road region, identify actual road texture information depending on the geographical area and weather conditions, the drivable road region identification should neglect the obstacles that are present in the input scene to focus only on the drivable road region for collision free smooth navigation and finally, the system should be robust and self-adapting to safely navigate through the drivable road regions.
Currently available solutions use different schemes for road region extraction but works only on structured roads or fails in meeting the road region detection accuracy. Hence, novel system and method is needed for robust road region extraction for safe autonomous vehicle navigation.